code: def __init__(
      self, logical_operator, is_classifier, n_estimators, classes
  ):
      super(Bagging, self).__init__(logical_operator, transformer=True)
  
      self.is_classifier = is_classifier
      self.n_estimators = float(n_estimators)
      self.perform_class_select = False
      if min(classes) != 0 or max(classes) != len(classes) - 1:
          self.perform_class_select = True
  
      self.binary_classification = False
      if len(classes) == 2:
          self.binary_classification = True
  
file_path: hummingbird/hummingbird/ml/operator_converters/sklearn/bagging.py
imports: from packaging.version import Version, parse
  import numpy
  from onnxconverter_common.registration import register_converter
  import torch
  from .._physical_operator import PhysicalOperator
label_desc: Use image node_id 1 to create a new Bagging object from inherited base classes: PhysicalOperator, torch.nn with example: obj = Bagging(logical_operator, is_classifier, n_estimators, classes)